code: def _sample_decode(
      model,
      global_step,
      sess,
      hparams,
      iterator,
      src_data,
      tgt_data,
      iterator_src_placeholder,
      iterator_batch_size_placeholder,
      summary_writer,
  ):
      """Pick a sentence and decode."""
      decode_id = random.randint(0, len(src_data) - 1)
      utils.print_out("  # %d" % decode_id)
  
      iterator_feed_dict = {
          iterator_src_placeholder: [src_data[decode_id]],
          iterator_batch_size_placeholder: 1,
      }
      sess.run(iterator.initializer, feed_dict=iterator_feed_dict)
  
      nmt_outputs, attention_summary = model.decode(sess)
  
      if hparams.infer_mode == "beam_search":
          # get the top translation.
          nmt_outputs = nmt_outputs[0]
  
      translation = nmt_utils.get_translation(
          nmt_outputs,
          sent_id=0,
          tgt_eos=hparams.eos,
          subword_option=hparams.subword_option,
      )
      utils.print_out("    src: %s" % src_data[decode_id])
      utils.print_out("    ref: %s" % tgt_data[decode_id])
      utils.print_out(b"    nmt: " + translation)
  
      # Summary
      if attention_summary is not None:
          summary_writer.add_summary(attention_summary, global_step)
  
imports: from __future__ import print_function
  import math
  import os
  import random
  import time
  import tensorflow
  from .None import inference, gnmt_model
  from .gnmt import attention_model, model
  from .gnmt import model_helper
  from .gnmt.utils import nmt_utils, misc_utils
label_desc: Use image node_id 18 for calling a global function with example usage: _sample_decode(model, global_step, sess, hparams, iterator, src_data, tgt_data, iterator_src_placeholder, iterator_batch_size_placeholder, summary_writer) without return types